Patient's profile(a):
The aim of this study is to identify the gene expression
changes associated with an NF1-related tumorgenesis with the
comparasion of ST88-14 and HSC using Agilent microarrays. The up
and down regulated genes from the diferential analysis are used in
NFFinder as the patient’s profile.

Drug's profile(b):
In this case the aim is to observe the expression changes
in different drug treatments. The data used as a profile are the
up- and down-regulated genes of the comparison by microarrays of
cells treated with PD901/JQ1 and with DMSO.

Patient's profile(c):
The profile used as input are the upregulated miRNAs
obtained from the comparison of bening menignomas and arachnoidal
tissue controls with miRNA arrays.

How to start interpreting your results depending on the databases selected for the analysis.

DrugMatrix/CMap

When we use DrugMatrix and CMap all the experiments in the
results are reasonable. This is because a control and a tested drug
are compared to obtain the profile. In this case there are 775 drug
experiments entries with 391 compounds. A good way to start is
selecting only some of the experiments to be further visualized. The
criterion to select a smaller list depends on the purpose of the
analysis (depending on the score, the number of times that a drug
appears in the results, the condition of the experiment, etc.). The
table is divided in different columns to an easier visualization and
has options to make the filtering possible.

GEO

In this case the interpretation needs some manual curation due to
NFFinder compares all possible profiles available in GEO datasets
and sometimes these have no sense. The marked experiments in the
figure below must be filtered, because the profiles are made from
time series (300 min vs 17 min and 6h vs 3h) and dose responses that
are not logic for this investigation. NFFinder allows you to filter
by condition, but sometimes there are interesting experiments as the
one in the red box that you might lost if you filter the results
directly.

From these tables some visualizations are generated to represent
the results in the different scenarios.

APPLICATION 1: Searching for drugs to treat a particular
disease.

1. Fill the input boxes with the up- and down-regulated genes
of the patient’s profile.

Trichostatin A appears in more than 75 experiments with scores
between 60 and 80.

The DRUGS related to experiments page displays the different
drugs that have an opposite profile than the introduced one. As the
figure shows, Trichostatin A appears in more than 75 experiments with a
medium score, which means that we can consider it a hypothetical drug
to revert the phenotype. On the right side of the page there is
information to study the conditions of the experiment.

APPLICATION 2: Looking for diseases similar to the input.

Drugs already known to treat a disease might be candidate to treat
diseases with similar gene expression profiles.

1. Fill the input boxes with the up- and down-regulated genes
of the patient’s profile.

The marked diseases appear several times in the results (10-18
times). You should discern which are interesting for the study.
Remark: Given that disease names were obtained from Metamap, we
recommend a careful examination.

The DISEASES related to experiments page displays the
different diseases that have a similar profile than the introduced one.
We select few diseases in which we are interested because of its score
and the several times that appear on the results. Next we will examine
the data through the table of the right of the page.

APPLICATION 3: Searching for expert authors in similar
diseases or biological processes related to our experiment.

Go to the Experts page with the same input as the one used in
the previous section.

The Experts page shows up the different authors appearing in
the results. The size of the boxes ranks the expert’s list according to
the articles in the results. An FQ, Campitello N. and Renne R. appear
in two studies despite the fact that Domany E. gets 4 profiles in one
study. The table of References links to the research pubmed article.
Remark: Several studies have more than one experiment, so
different profiles are compared.

APPLICATION 4: Looking for drugs similar to the input.

1. Fill the input boxes with the up- and down-regulated genes
of the drug’s profile.

Trichostatin A: appears in more than 65 experiments with a
score between 60 and 80. This suggests that this drug has a similar
profile.

The DRUGS related to experiments page displays the different
drugs that have a similar profile. As the figure shows, Trichostatin A
appears several times with a score between 60 and 80. In this case it
is important to look at the score to analyze how similar they are.

APPLICATION 5: Searching for opposite drugs to a known
drug

Another useful application is to search for opposite drugs profiles
to the tested drug. By changing the profile matching to inverse and
following the previous steps you will be able to visualize this
information.

APPLICATION 6: Looking for diseases to be treated with a
tested drug.

1. Fill the input boxes with up- and down-regulated genes of
the drug’s profile.

These are the diseases with a transcriptional genetic profile
opposite from the drug’s profile. It helps us to generate hypothesis
about what diseases can be treated with this drug. Here we obtain some
diseases with high score which means that are very similar to the
introduced profile.

The DISEASES related to experiments page displays the
different diseases that have an opposite profile than the drug’s one.
The next step is to study if those diseases could be treated with the
drug. The table on the right side of the page provides you information
about how was carried out the experiment and if it fits for the
purpose.

APPLICATION 7: Drugs to treat a disease were known miRNAs
are involved

The data is from menignomas, so we are going to start focusing on
the selected drugs.

The DRUGS related to experiments page displays the different
drugs that have an opposite profile to the targets of the miRNAs
introduced. These targets are inferred as a list of down-regulated
genes to create the profile. As commented in the previous examples you
must study the different results to ensure they match with your
expectations.

As the figure below details, several types of cancer with high score
are similar to the profile introduced, so the targets of the miRNAs are
upregulated in these diseases.

The Diseases related to experiments page displays those
experiments with the same down-regulated genes as the ones that should
appear in those experiments that miRNAs are involved. As in other
cases, you can find information about the experiment in the table on
the right side.